The research on optimization of top-k SPARQL query would largely benefit from the establishment of a benchmark that allows comparing different approaches. For such a benchmark to be meaningful, at least two requirements should hold: 1) the benchmark should resemble reality as much as possible, and 2) it should stress the features of the topk SPARQL queries both from a syntactic and performance perspective. In this paper we propose Top-k DBPSB: an extension of the DBpedia SPARQL benchmark (DBPSB), a benchmark known to resemble reality, with the capabilities required to compare SPARQL engines on top-k queries.

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The research on optimization of top-k SPARQL query would largely benefit from the establishment of a benchmark that allows comparing different approaches. For such a benchmark to be meaningful, at least two requirements should hold: 1) the benchmark should resemble reality as much as possible, and 2) it should stress the features of the topk SPARQL queries both from a syntactic and performance perspective. In this paper we propose Top-k DBPSB: an extension of the DBpedia SPARQL benchmark (DBPSB), a benchmark known to resemble reality, with the capabilities required to compare SPARQL engines on top-k queries.@en

More and more applications require real-time processing of massive, dynamically generated, ordered data; order is an essential factor as it reflects recency or relevance. Semantic technologies risk being unable to meet the needs of such applications, as they are not equipped with the appropriate instruments for answering queries over massive, highly dynamic, ordered data sets. In this vision paper, we argue that some data management techniques should be exported to the context of semantic technologies, by integrating ordering with reasoning, and by using methods which are inspired by stream and rank-aware data management. We systematically explore the problem space, and point both to problems which have been successfully approached and to problems which still need fundamental research, in an attempt to stimulate and guide a paradigm shift in semantic technologies.@en

An increasing number of open data sets is becoming available on the Web as Linked Data (LD), many efforts has been devoted to show the potential of LD applications from the technical point of view. However, less attention has been paid to the analysis of the information seeking requirements from the user point of view. In this paper we examine the Information Seeking Process and we propose a general framework that address all its requirements in the context of LD-based applications. We support seamless integration of both Linked and non-Linked data sources and we allow designers to define complex, rank-aware result construction and exploration rules based on rank aggregation and multiple many-to-many data navigation.@en

Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The PARQL-ANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for PARQL-ANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a PARQL-ANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.

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With the widespread adoption of Linked Data, the efficient processing of SPARQL queries gains importance. A crucial category of queries that is prone to optimization is "top-k" queries, i.e. queries returning the top k results ordered by a specified ranking function. Top-k queries can be expressed in SPARQL by appending to a SELECT query the ORDER BY and LIMIT clauses, which impose a sorting order on the result set, and limit the number of results. However, the ORDER BY and LIMIT clauses in SPARQL algebra are result modifiers, i.e. their evaluation is performed only after the evaluation of the other query clauses. The evaluation of ORDER BY and LIMIT clauses in SPARQL engines typically requires the process of all the matching solutions (possibly thousands), followed by a monolithically computation of the ranking function for each solution, even if only a limited number (e.g. K = 10) of them were requested, thus leading to poor performance. In this paper, we present SPARQL-RANK, an extension of the SPARQL algebra and execution model that supports ranking as a firstclass SPAR-QL construct. The new algebra and execution model allow for splitting the ranking function and interleaving it with other operations. We also provide a prototypal open source implementation of SPARQL-RANK based on ARQ, and we carry out a series of preliminary experiments.@en

In the Search Computing project, Web services are modeled by the Semantic Resource Framework (SRF). In this article, we argue that the SRF could benefit from ontological concepts borrowed from the Semantic Web. We first present the knowledge representation used in the Semantic Web, notably in the YAGO ontology [14]. We show how this model is used in the ANGIE system [12] to represent Web Services in conjunction with YAGO. We draw parallels to the Service Mart [3] model used in SeCo. We propose a symbiosis of the two models, discussing the challenges and advantages that come with the integrated model.@en

Search Computing defines a new class of applications, which enable end users to perform exploratory search processes over multi-domain data sources available on the Web. These applications exploit suitable software frameworks and models that make it possible for expert users to configure the data sources to be searched and the interfaces for query submission and result visualization. We describe some usage scenarios and the reference architecture for Search Computing systems.@en

Search computing focuses on building answers to complex search queries (for example, "Where can I attend an interesting conference in my field near a sunny beach?") by interacting with a constellation of cooperating search services, and using result ranking and joining as the dominant factors for service composition. The service computing paradigm has so far been neutral to the specific features of search applications and services. To address this weakness, search computing advocates a new approach in which search, join, and ranking are the central aspects for service composition.

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